Overview

Dataset statistics

Number of variables33
Number of observations81412
Missing cells6682
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 MiB
Average record size in memory202.0 B

Variable types

Numeric10
Categorical22
Boolean1

Alerts

diag_1 has a high cardinality: 701 distinct values High cardinality
diag_2 has a high cardinality: 725 distinct values High cardinality
diag_3 has a high cardinality: 761 distinct values High cardinality
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
admission_source_code is highly correlated with admission_type_codeHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
insulin is highly correlated with diabetesMed and 1 other fieldsHigh correlation
admission_type_code is highly correlated with admission_source_codeHigh correlation
change is highly correlated with diabetesMed and 1 other fieldsHigh correlation
gender is highly correlated with hemoglobin_levelHigh correlation
admission_type_code is highly correlated with admission_source_codeHigh correlation
admission_source_code is highly correlated with admission_type_code and 2 other fieldsHigh correlation
medical_specialty is highly correlated with admission_source_codeHigh correlation
hemoglobin_level is highly correlated with genderHigh correlation
max_glu_serum is highly correlated with admission_source_codeHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
admission_type_code has 1162 (1.4%) missing values Missing
num_lab_procedures has 1493 (1.8%) missing values Missing
num_medications has 2678 (3.3%) missing values Missing
diag_2 has 1349 (1.7%) missing values Missing
num_procedures has 37355 (45.9%) zeros Zeros
number_outpatient has 67984 (83.5%) zeros Zeros
number_emergency has 72350 (88.9%) zeros Zeros
number_inpatient has 53995 (66.3%) zeros Zeros

Reproduction

Analysis started2022-02-01 18:26:58.180676
Analysis finished2022-02-01 18:27:33.856248
Duration35.68 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

patient_id
Real number (ℝ≥0)

Distinct60069
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108639531.1
Minimum198
Maximum379005166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:34.015701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile2914233.3
Q146839060
median90834372
Q3175111722
95-th percentile222996661.2
Maximum379005166
Range379004968
Interquartile range (IQR)128272662

Descriptive statistics

Standard deviation77324533.16
Coefficient of variation (CV)0.7117531928
Kurtosis-0.3648068986
Mean108639531.1
Median Absolute Deviation (MAD)65849112
Skewness0.4666683655
Sum8.844561509 × 1012
Variance5.979083428 × 1015
MonotonicityNot monotonic
2022-02-01T18:27:34.242076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17757171033
 
< 0.1%
17645500820
 
< 0.1%
18121953619
 
< 0.1%
18541863018
 
< 0.1%
4728673818
 
< 0.1%
332051418
 
< 0.1%
18232048817
 
< 0.1%
4639797017
 
< 0.1%
8628174016
 
< 0.1%
16885715416
 
< 0.1%
Other values (60059)81220
99.8%
ValueCountFrequency (%)
1982
< 0.1%
6841
 
< 0.1%
13861
 
< 0.1%
14761
 
< 0.1%
17821
 
< 0.1%
22324
< 0.1%
25381
 
< 0.1%
25563
< 0.1%
31861
 
< 0.1%
39781
 
< 0.1%
ValueCountFrequency (%)
3790051661
< 0.1%
3787021181
< 0.1%
3786987881
< 0.1%
3786641021
< 0.1%
3785156201
< 0.1%
3784314521
< 0.1%
3783585701
< 0.1%
3782586341
< 0.1%
3781945901
< 0.1%
3781511561
< 0.1%

race
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
white
60873 
black
15388 
unknown/other
 
3007
hispanic
 
1627
asian
 
517

Length

Max length13
Median length5
Mean length5.355439002
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowblack
5th rowwhite

Common Values

ValueCountFrequency (%)
white60873
74.8%
black15388
 
18.9%
unknown/other3007
 
3.7%
hispanic1627
 
2.0%
asian517
 
0.6%

Length

2022-02-01T18:27:34.454846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:34.598630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
white60873
74.8%
black15388
 
18.9%
unknown/other3007
 
3.7%
hispanic1627
 
2.0%
asian517
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
female
43719 
male
37691 
unknown
 
2

Length

Max length7
Median length6
Mean length5.074092271
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowfemale
5th rowfemale

Common Values

ValueCountFrequency (%)
female43719
53.7%
male37691
46.3%
unknown2
 
< 0.1%

Length

2022-02-01T18:27:34.708610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:34.841867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
female43719
53.7%
male37691
46.3%
unknown2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.9 KiB
70-80
20261 
60-70
17414 
50-60
13414 
80-90
13383 
40-50
7498 
Other values (6)
9442 

Length

Max length7
Median length5
Mean length5.082395716
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50-60
2nd row80-90
3rd row60-70
4th row70-80
5th rowunknown

Common Values

ValueCountFrequency (%)
70-8020261
24.9%
60-7017414
21.4%
50-6013414
16.5%
80-9013383
16.4%
40-507498
 
9.2%
30-402964
 
3.6%
unknown2336
 
2.9%
90-1002172
 
2.7%
20-301297
 
1.6%
10-20537
 
0.7%

Length

2022-02-01T18:27:34.939810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
70-8020261
24.9%
60-7017414
21.4%
50-6013414
16.5%
80-9013383
16.4%
40-507498
 
9.2%
30-402964
 
3.6%
unknown2336
 
2.9%
90-1002172
 
2.7%
20-301297
 
1.6%
10-20537
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

weight
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.9 KiB
unknown
78913 
75-100
 
1037
50-75
 
713
100-125
 
482
125-150
 
117
Other values (5)
 
150

Length

Max length7
Median length7
Mean length6.966393161
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown78913
96.9%
75-1001037
 
1.3%
50-75713
 
0.9%
100-125482
 
0.6%
125-150117
 
0.1%
25-5072
 
0.1%
0-2540
 
< 0.1%
150-17527
 
< 0.1%
175-2008
 
< 0.1%
>2003
 
< 0.1%

Length

2022-02-01T18:27:35.134814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:35.289579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown78913
96.9%
75-1001037
 
1.3%
50-75713
 
0.9%
100-125482
 
0.6%
125-150117
 
0.1%
25-5072
 
0.1%
0-2540
 
< 0.1%
150-17527
 
< 0.1%
175-2008
 
< 0.1%
2003
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

admission_type_code
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing1162
Missing (%)1.4%
Memory size715.8 KiB
emergency
42562 
elective
14884 
urgent
14576 
n/a
8204 
trauma
 
16

Length

Max length9
Median length9
Mean length7.655451713
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowelective
2nd rowurgent
3rd rowemergency
4th rown/a
5th rowemergency

Common Values

ValueCountFrequency (%)
emergency42562
52.3%
elective14884
 
18.3%
urgent14576
 
17.9%
n/a8204
 
10.1%
trauma16
 
< 0.1%
newborn8
 
< 0.1%
(Missing)1162
 
1.4%

Length

2022-02-01T18:27:35.433023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:35.572630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
emergency42562
53.0%
elective14884
 
18.5%
urgent14576
 
18.2%
n/a8204
 
10.2%
trauma16
 
< 0.1%
newborn8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.9 KiB
discharged_home
47854 
transferred_inpatient
16501 
home_care
10329 
unknown
 
4276
expired
 
1317
Other values (3)
 
1135

Length

Max length22
Median length15
Mean length14.88597504
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdischarged_home
2nd rowdischarged_home
3rd rowdischarged_home
4th rowdischarged_home
5th rowtransferred_inpatient

Common Values

ValueCountFrequency (%)
discharged_home47854
58.8%
transferred_inpatient16501
 
20.3%
home_care10329
 
12.7%
unknown4276
 
5.3%
expired1317
 
1.6%
discharged_hospice608
 
0.7%
left_ama506
 
0.6%
transferred_outpatient21
 
< 0.1%

Length

2022-02-01T18:27:35.691371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:35.837008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
discharged_home47854
58.8%
transferred_inpatient16501
 
20.3%
home_care10329
 
12.7%
unknown4276
 
5.3%
expired1317
 
1.6%
discharged_hospice608
 
0.7%
left_ama506
 
0.6%
transferred_outpatient21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

admission_source_code
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.7 KiB
emergency
45942 
referral
24710 
unknown
5661 
transfer
5099 

Length

Max length9
Median length9
Mean length8.494779639
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreferral
2nd rowemergency
3rd rowemergency
4th rowunknown
5th rowemergency

Common Values

ValueCountFrequency (%)
emergency45942
56.4%
referral24710
30.4%
unknown5661
 
7.0%
transfer5099
 
6.3%

Length

2022-02-01T18:27:35.973030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:36.112267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
emergency45942
56.4%
referral24710
30.4%
unknown5661
 
7.0%
transfer5099
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395924434
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:36.198539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.975844099
Coefficient of variation (CV)0.6769552444
Kurtosis0.8490950317
Mean4.395924434
Median Absolute Deviation (MAD)2
Skewness1.130728188
Sum357881
Variance8.855648103
MonotonicityNot monotonic
2022-02-01T18:27:36.372916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
314223
17.5%
213723
16.9%
111302
13.9%
411228
13.8%
57975
9.8%
66059
7.4%
74717
 
5.8%
83528
 
4.3%
92372
 
2.9%
101886
 
2.3%
Other values (4)4399
 
5.4%
ValueCountFrequency (%)
111302
13.9%
213723
16.9%
314223
17.5%
411228
13.8%
57975
9.8%
66059
7.4%
74717
 
5.8%
83528
 
4.3%
92372
 
2.9%
101886
 
2.3%
ValueCountFrequency (%)
14814
 
1.0%
13944
 
1.2%
121153
 
1.4%
111488
 
1.8%
101886
 
2.3%
92372
 
2.9%
83528
4.3%
74717
5.8%
66059
7.4%
57975
9.8%

payer_code
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.7 KiB
insured
45131 
unknown
32278 
SP
 
4003

Length

Max length7
Median length7
Mean length6.754151722
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowinsured
3rd rowinsured
4th rowunknown
5th rowinsured

Common Values

ValueCountFrequency (%)
insured45131
55.4%
unknown32278
39.6%
SP4003
 
4.9%

Length

2022-02-01T18:27:36.558725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:36.696073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
insured45131
55.4%
unknown32278
39.6%
sp4003
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

medical_specialty
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.3 KiB
unknown
40020 
internalmedicine
11712 
emergency/trauma
6021 
family/generalpractice
5939 
cardiology
4273 
Other values (20)
13447 

Length

Max length33
Median length7
Mean length11.40828133
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowemergency/trauma
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown40020
49.2%
internalmedicine11712
 
14.4%
emergency/trauma6021
 
7.4%
family/generalpractice5939
 
7.3%
cardiology4273
 
5.2%
surgery-general2473
 
3.0%
nephrology1299
 
1.6%
other1187
 
1.5%
orthopedics1100
 
1.4%
orthopedics-reconstructive981
 
1.2%
Other values (15)6407
 
7.9%

Length

2022-02-01T18:27:36.817997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown40020
49.2%
internalmedicine11712
 
14.4%
emergency/trauma6021
 
7.4%
family/generalpractice5939
 
7.3%
cardiology4273
 
5.2%
surgery-general2473
 
3.0%
nephrology1299
 
1.6%
other1187
 
1.5%
orthopedics1100
 
1.4%
orthopedics-reconstructive981
 
1.2%
Other values (15)6407
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

has_prosthesis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
80550 
1
 
862

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
080550
98.9%
1862
 
1.1%

Length

2022-02-01T18:27:37.059281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:37.203771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
080550
98.9%
1862
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Complete
67114 
Incomplete
13978 
None
 
320

Length

Max length10
Median length8
Mean length8.327666683
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowComplete
3rd rowComplete
4th rowComplete
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete67114
82.4%
Incomplete13978
 
17.2%
None320
 
0.4%

Length

2022-02-01T18:27:37.310193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:37.448906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
complete67114
82.4%
incomplete13978
 
17.2%
none320
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_lab_procedures
Real number (ℝ≥0)

MISSING

Distinct115
Distinct (%)0.1%
Missing1493
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean43.07119709
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:37.563694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q132
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.63040493
Coefficient of variation (CV)0.4557664114
Kurtosis-0.2441755168
Mean43.07119709
Median Absolute Deviation (MAD)13
Skewness-0.2404533528
Sum3442207
Variance385.3527977
MonotonicityNot monotonic
2022-02-01T18:27:37.808531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12533
 
3.1%
432207
 
2.7%
441988
 
2.4%
451884
 
2.3%
381764
 
2.2%
401736
 
2.1%
461720
 
2.1%
411718
 
2.1%
471679
 
2.1%
391662
 
2.0%
Other values (105)61028
75.0%
ValueCountFrequency (%)
12533
3.1%
2858
 
1.1%
3523
 
0.6%
4285
 
0.4%
5231
 
0.3%
6214
 
0.3%
7258
 
0.3%
8281
 
0.3%
9728
 
0.9%
10655
 
0.8%
ValueCountFrequency (%)
1321
 
< 0.1%
1261
 
< 0.1%
1211
 
< 0.1%
1181
 
< 0.1%
1142
< 0.1%
1131
 
< 0.1%
1112
< 0.1%
1093
< 0.1%
1083
< 0.1%
1064
< 0.1%

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.341767798
Minimum0
Maximum6
Zeros37355
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:37.992225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.708464781
Coefficient of variation (CV)1.273293921
Kurtosis0.8445664498
Mean1.341767798
Median Absolute Deviation (MAD)1
Skewness1.313355194
Sum109236
Variance2.918851909
MonotonicityNot monotonic
2022-02-01T18:27:38.165613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
037355
45.9%
116513
20.3%
210162
 
12.5%
37548
 
9.3%
63994
 
4.9%
43409
 
4.2%
52431
 
3.0%
ValueCountFrequency (%)
037355
45.9%
116513
20.3%
210162
 
12.5%
37548
 
9.3%
43409
 
4.2%
52431
 
3.0%
63994
 
4.9%
ValueCountFrequency (%)
63994
 
4.9%
52431
 
3.0%
43409
 
4.2%
37548
 
9.3%
210162
 
12.5%
116513
20.3%
037355
45.9%

num_medications
Real number (ℝ≥0)

MISSING

Distinct73
Distinct (%)0.1%
Missing2678
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean16.02442401
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:38.506179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.107234783
Coefficient of variation (CV)0.5059298717
Kurtosis3.427614554
Mean16.02442401
Median Absolute Deviation (MAD)5
Skewness1.316139858
Sum1261667
Variance65.72725582
MonotonicityNot monotonic
2022-02-01T18:27:38.878537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134714
 
5.8%
124636
 
5.7%
114457
 
5.5%
154443
 
5.5%
144436
 
5.4%
164234
 
5.2%
104108
 
5.0%
173817
 
4.7%
93813
 
4.7%
183523
 
4.3%
Other values (63)36553
44.9%
ValueCountFrequency (%)
1203
 
0.2%
2357
 
0.4%
3687
 
0.8%
41107
 
1.4%
51570
 
1.9%
62094
2.6%
72662
3.3%
83356
4.1%
93813
4.7%
104108
5.0%
ValueCountFrequency (%)
811
 
< 0.1%
752
 
< 0.1%
741
 
< 0.1%
702
 
< 0.1%
695
< 0.1%
686
< 0.1%
676
< 0.1%
663
 
< 0.1%
658
< 0.1%
647
< 0.1%

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3709526851
Minimum0
Maximum42
Zeros67984
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:39.177268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.278537861
Coefficient of variation (CV)3.446633256
Kurtosis152.4729964
Mean0.3709526851
Median Absolute Deviation (MAD)0
Skewness8.984331589
Sum30200
Variance1.634659062
MonotonicityNot monotonic
2022-02-01T18:27:39.390405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
067984
83.5%
16862
 
8.4%
22904
 
3.6%
31618
 
2.0%
4873
 
1.1%
5420
 
0.5%
6238
 
0.3%
7129
 
0.2%
876
 
0.1%
973
 
0.1%
Other values (29)235
 
0.3%
ValueCountFrequency (%)
067984
83.5%
16862
 
8.4%
22904
 
3.6%
31618
 
2.0%
4873
 
1.1%
5420
 
0.5%
6238
 
0.3%
7129
 
0.2%
876
 
0.1%
973
 
0.1%
ValueCountFrequency (%)
421
< 0.1%
401
< 0.1%
391
< 0.1%
381
< 0.1%
371
< 0.1%
361
< 0.1%
351
< 0.1%
341
< 0.1%
332
< 0.1%
292
< 0.1%

number_emergency
Real number (ℝ≥0)

ZEROS

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1975875792
Minimum0
Maximum64
Zeros72350
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:39.605661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8812896989
Coefficient of variation (CV)4.460248475
Kurtosis638.5493694
Mean0.1975875792
Median Absolute Deviation (MAD)0
Skewness16.40885167
Sum16086
Variance0.7766715333
MonotonicityNot monotonic
2022-02-01T18:27:40.054068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
072350
88.9%
16064
 
7.4%
21646
 
2.0%
3588
 
0.7%
4292
 
0.4%
5167
 
0.2%
682
 
0.1%
755
 
0.1%
839
 
< 0.1%
927
 
< 0.1%
Other values (19)102
 
0.1%
ValueCountFrequency (%)
072350
88.9%
16064
 
7.4%
21646
 
2.0%
3588
 
0.7%
4292
 
0.4%
5167
 
0.2%
682
 
0.1%
755
 
0.1%
839
 
< 0.1%
927
 
< 0.1%
ValueCountFrequency (%)
641
 
< 0.1%
461
 
< 0.1%
421
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
252
< 0.1%
241
 
< 0.1%
224
< 0.1%
212
< 0.1%
204
< 0.1%

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6377929544
Minimum0
Maximum21
Zeros53995
Zeros (%)66.3%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:40.220824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.265472414
Coefficient of variation (CV)1.984142981
Kurtosis20.83213029
Mean0.6377929544
Median Absolute Deviation (MAD)0
Skewness3.619760625
Sum51924
Variance1.60142043
MonotonicityNot monotonic
2022-02-01T18:27:40.426947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
053995
66.3%
115706
 
19.3%
26057
 
7.4%
32747
 
3.4%
41288
 
1.6%
5641
 
0.8%
6386
 
0.5%
7224
 
0.3%
8120
 
0.1%
993
 
0.1%
Other values (11)155
 
0.2%
ValueCountFrequency (%)
053995
66.3%
115706
 
19.3%
26057
 
7.4%
32747
 
3.4%
41288
 
1.6%
5641
 
0.8%
6386
 
0.5%
7224
 
0.3%
8120
 
0.1%
993
 
0.1%
ValueCountFrequency (%)
211
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
164
 
< 0.1%
157
 
< 0.1%
146
 
< 0.1%
1316
 
< 0.1%
1230
< 0.1%
1140
< 0.1%

diag_1
Categorical

HIGH CARDINALITY

Distinct701
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
428
 
5429
414
 
5270
786
 
3232
410
 
2914
486
 
2776
Other values (696)
61791 

Length

Max length6
Median length3
Mean length3.17798359
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)0.1%

Sample

1st row721
2nd row276
3rd row414
4th row577
5th row531

Common Values

ValueCountFrequency (%)
4285429
 
6.7%
4145270
 
6.5%
7863232
 
4.0%
4102914
 
3.6%
4862776
 
3.4%
4272218
 
2.7%
4911811
 
2.2%
7151691
 
2.1%
7801620
 
2.0%
6821612
 
2.0%
Other values (691)52839
64.9%

Length

2022-02-01T18:27:40.646986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4285429
 
6.7%
4145270
 
6.5%
7863232
 
4.0%
4102914
 
3.6%
4862776
 
3.4%
4272218
 
2.7%
4911811
 
2.2%
7151691
 
2.1%
7801620
 
2.0%
6821612
 
2.0%
Other values (691)52839
64.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_2
Categorical

HIGH CARDINALITY
MISSING

Distinct725
Distinct (%)0.9%
Missing1349
Missing (%)1.7%
Memory size1.2 MiB
276
 
5296
428
 
5234
250
 
4778
427
 
3949
401
 
2906
Other values (720)
57900 

Length

Max length6
Median length3
Mean length3.166931042
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)0.2%

Sample

1st row250.6
2nd row507
3rd row490
4th row585
5th row532

Common Values

ValueCountFrequency (%)
2765296
 
6.5%
4285234
 
6.4%
2504778
 
5.9%
4273949
 
4.9%
4012906
 
3.6%
4962626
 
3.2%
5992604
 
3.2%
4032252
 
2.8%
4142076
 
2.5%
4112019
 
2.5%
Other values (715)46323
56.9%

Length

2022-02-01T18:27:40.839618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2765296
 
6.6%
4285234
 
6.5%
2504778
 
6.0%
4273949
 
4.9%
4012906
 
3.6%
4962626
 
3.3%
5992604
 
3.3%
4032252
 
2.8%
4142076
 
2.6%
4112019
 
2.5%
Other values (715)46323
57.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_3
Categorical

HIGH CARDINALITY

Distinct761
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
250
9166 
401
6606 
276
 
4112
428
 
3734
427
 
3156
Other values (756)
54638 

Length

Max length6
Median length3
Mean length3.111678868
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)0.1%

Sample

1st row357
2nd row150
3rd row250
4th row250
5th row562

Common Values

ValueCountFrequency (%)
2509166
 
11.3%
4016606
 
8.1%
2764112
 
5.1%
4283734
 
4.6%
4273156
 
3.9%
4142938
 
3.6%
4962090
 
2.6%
4031886
 
2.3%
5851621
 
2.0%
2721605
 
2.0%
Other values (751)44498
54.7%

Length

2022-02-01T18:27:41.014647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2509166
 
11.3%
4016606
 
8.1%
2764112
 
5.1%
4283734
 
4.6%
4273156
 
3.9%
4142938
 
3.6%
4962090
 
2.6%
4031886
 
2.3%
5851621
 
2.0%
2721605
 
2.0%
Other values (751)44498
54.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_diagnoses
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.421964821
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:41.131631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.931480463
Coefficient of variation (CV)0.2602384287
Kurtosis-0.07655270384
Mean7.421964821
Median Absolute Deviation (MAD)1
Skewness-0.8713996895
Sum604237
Variance3.730616778
MonotonicityNot monotonic
2022-02-01T18:27:41.255973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
939501
48.5%
59144
 
11.2%
88506
 
10.4%
78379
 
10.3%
68133
 
10.0%
44428
 
5.4%
32238
 
2.7%
2824
 
1.0%
1167
 
0.2%
1638
 
< 0.1%
Other values (6)54
 
0.1%
ValueCountFrequency (%)
1167
 
0.2%
2824
 
1.0%
32238
 
2.7%
44428
 
5.4%
59144
 
11.2%
68133
 
10.0%
78379
 
10.3%
88506
 
10.4%
939501
48.5%
1014
 
< 0.1%
ValueCountFrequency (%)
1638
 
< 0.1%
158
 
< 0.1%
145
 
< 0.1%
1314
 
< 0.1%
125
 
< 0.1%
118
 
< 0.1%
1014
 
< 0.1%
939501
48.5%
88506
 
10.4%
78379
 
10.3%

blood_type
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
O+
32053 
A+
24744 
B+
9218 
O-
5689 
A-
4826 
Other values (3)
4882 

Length

Max length3
Median length2
Mean length2.041738319
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA+
2nd rowB+
3rd rowO+
4th rowAB-
5th rowA+

Common Values

ValueCountFrequency (%)
O+32053
39.4%
A+24744
30.4%
B+9218
 
11.3%
O-5689
 
7.0%
A-4826
 
5.9%
AB+2619
 
3.2%
B-1484
 
1.8%
AB-779
 
1.0%

Length

2022-02-01T18:27:41.400214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:41.496442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
o37742
46.4%
a29570
36.3%
b10702
 
13.1%
ab3398
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hemoglobin_level
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.19232791
Minimum10.5
Maximum18.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-02-01T18:27:41.635905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile12.6
Q113.4
median14.1
Q315
95-th percentile16
Maximum18.6
Range8.1
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.059999933
Coefficient of variation (CV)0.07468823574
Kurtosis-0.4492654524
Mean14.19232791
Median Absolute Deviation (MAD)0.8
Skewness0.1878605037
Sum1155425.8
Variance1.123599858
MonotonicityNot monotonic
2022-02-01T18:27:41.851336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.62964
 
3.6%
13.92911
 
3.6%
13.72876
 
3.5%
13.82841
 
3.5%
13.52796
 
3.4%
14.12745
 
3.4%
142730
 
3.4%
14.22657
 
3.3%
13.42654
 
3.3%
13.32643
 
3.2%
Other values (67)53595
65.8%
ValueCountFrequency (%)
10.52
 
< 0.1%
10.82
 
< 0.1%
10.93
 
< 0.1%
115
 
< 0.1%
11.13
 
< 0.1%
11.218
 
< 0.1%
11.317
 
< 0.1%
11.433
< 0.1%
11.547
0.1%
11.671
0.1%
ValueCountFrequency (%)
18.61
 
< 0.1%
18.22
 
< 0.1%
18.12
 
< 0.1%
181
 
< 0.1%
17.92
 
< 0.1%
17.82
 
< 0.1%
17.72
 
< 0.1%
17.67
 
< 0.1%
17.516
< 0.1%
17.432
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
71697 
1
9715 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
071697
88.1%
19715
 
11.9%

Length

2022-02-01T18:27:42.060181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:42.185962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
071697
88.1%
19715
 
11.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

max_glu_serum
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.7 KiB
unknown
77159 
norm
 
2049
>200
 
1179
>300
 
1025

Length

Max length7
Median length7
Mean length6.843278632
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown77159
94.8%
norm2049
 
2.5%
>2001179
 
1.4%
>3001025
 
1.3%

Length

2022-02-01T18:27:42.283140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:42.450227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown77159
94.8%
norm2049
 
2.5%
2001179
 
1.4%
3001025
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.7 KiB
unknown
67807 
>8
 
6547
norm
 
4003
>7
 
3055

Length

Max length7
Median length7
Mean length6.26277453
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd row>7
3rd rowunknown
4th row>8
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown67807
83.3%
>86547
 
8.0%
norm4003
 
4.9%
>73055
 
3.8%

Length

2022-02-01T18:27:42.555212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:42.694045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown67807
83.3%
86547
 
8.0%
norm4003
 
4.9%
73055
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diuretics
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
79893 
1
 
1519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
079893
98.1%
11519
 
1.9%

Length

2022-02-01T18:27:42.792355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:42.930533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
079893
98.1%
11519
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

insulin
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
44360 
0
37052 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
144360
54.5%
037052
45.5%

Length

2022-02-01T18:27:43.022741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:43.158341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
144360
54.5%
037052
45.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

change
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
43772 
1
37640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
043772
53.8%
137640
46.2%

Length

2022-02-01T18:27:43.293288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:43.441910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
043772
53.8%
137640
46.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diabetesMed
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
62718 
0
18694 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
162718
77.0%
018694
 
23.0%

Length

2022-02-01T18:27:43.564064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T18:27:43.706258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
162718
77.0%
018694
 
23.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

readmitted
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size715.5 KiB
False
72340 
True
9072 
ValueCountFrequency (%)
False72340
88.9%
True9072
 
11.1%
2022-02-01T18:27:43.753531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2022-02-01T18:27:28.809917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:13.703505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:15.314429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:16.879789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:18.444729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:19.999438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:21.677952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:23.890429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:25.725351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:27.224054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:28.975649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:13.868810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:15.473051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:17.043575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:18.604934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:20.178462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:22.532377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:24.116651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:25.877856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:27.393013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:29.161626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:14.029488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:15.630865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:17.199185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:18.756299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:20.363104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:22.691401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:24.267845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:26.025420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:27.551922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:29.377470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:14.191119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:15.792640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:17.354970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:18.915055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:20.537467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:22.849323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:24.419084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:26.174407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:27.709166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:29.554893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:14.352718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:15.947189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:17.509977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:19.070003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:20.707099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:23.000228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:24.571986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:26.320914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:27.862065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:29.759204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:14.521111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:16.107960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:17.669929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:19.229421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:20.875679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:23.152833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:24.783493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:26.481209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:28.029059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:30.161964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:14.669804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:16.259994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:17.818382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:19.371794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:21.029446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:23.292000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:24.982241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:26.619017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:28.179068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:30.339870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:14.829938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:16.412756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:17.973794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:19.524855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:21.192457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:23.441314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:25.165721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:26.769624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:28.334687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:30.499690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:14.981816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:16.556136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:18.124400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:19.666430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:21.340052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:23.578077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:25.382805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:26.903519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:28.478775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:30.662085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:15.146269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:16.715345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:18.282357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:19.827674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:21.508507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:23.732256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:25.563468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:27.065193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T18:27:28.642820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-02-01T18:27:43.863327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-01T18:27:44.152145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-01T18:27:44.423777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-01T18:27:44.727901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-01T18:27:45.104473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-01T18:27:31.051238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-01T18:27:32.460079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-01T18:27:33.278322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-01T18:27:33.540855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

patient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedreadmitted
admission_id
0199042938whitemale50-60unknownelectivedischarged_homereferral1unknownunknown0Complete24.039.0000721250.63575A+14.50unknownunknown0001False
191962954whitemale80-90unknownurgentdischarged_homeemergency3insuredemergency/trauma0Complete50.008.00012765071509B+15.70unknown>70000True
2109707084whitefemale60-70unknownemergencydischarged_homeemergency5insuredunknown0Complete43.0628.00004144902506O+13.00unknownunknown0111False
3157495374blackfemale70-80unknownn/adischarged_homeunknown2unknownunknown0Complete58.058.00115775852509AB-13.50unknown>80001False
482692360whitefemaleunknownunknownemergencytransferred_inpatientemergency12insuredunknown0Complete56.0116.00025315325628A+13.00unknownunknown0000False
5218016576whitefemale70-80unknownurgentdischarged_homereferral4insuredunknown0Incomplete14.0313.00005782805629A+13.10unknownunknown0001True
6143084970whitemale60-70unknownemergencydischarged_homeemergency6insuredunknown0Complete62.0021.00004824282769A-14.20unknown>70101False
7227644092unknown/otherfemale70-80unknownelectivedischarged_homereferral11unknowninternalmedicine0Incomplete18.049.0101V574385996O+12.90unknownunknown0000False
877740434whitefemale70-80unknownemergencytransferred_inpatientemergency2insuredunknown0Complete36.009.0003250.86824966O-13.90unknownunknown0101False
9203123016whitefemale40-50unknownelectivedischarged_homereferral2unknownunknown0Complete5.0225.00004333624019A+13.20unknownunknown0111False

Last rows

patient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedreadmitted
admission_id
81402155324556unknown/othermale50-60unknownemergencydischarged_homeemergency3insuredunknown0Incomplete1.0013.0000682250.827079O+16.70unknownunknown0101False
81403224478630blackfemale80-90unknownelectivehome_carereferral7unknownunknown0Complete35.0237.00204407074014A+13.80unknownunknown0111True
81404228104460blackfemale50-60unknownelectivedischarged_homereferral3unknownobstetricsandgynecology0Complete8.0212.00002182186269AB-12.80unknownunknown0001False
8140548436146blackfemale40-50unknownemergencydischarged_homeemergency6insurednephrology0Incomplete35.0322.0000595591V459B+11.40unknownunknown0101False
8140691915884whitefemale80-90unknownemergencytransferred_inpatientemergency4insuredunknown0Complete46.0011.00005842762729A+13.80unknownunknown0101False
8140780746578whitemale60-70unknownemergencytransferred_inpatientemergency2insuredunknown0Complete64.0022.0003486496250.925O+15.40unknownunknown0111False
81408221853996blackfemale70-80unknownemergencyhome_careemergency8insuredinternalmedicine0Complete62.0219.00014404012769O+12.80unknownunknown0111False
81409104846580blackfemale60-70unknownemergencytransferred_inpatientemergency5unknownunknown0Complete1.0214.0000822403250.69B+13.00unknownunknown0000True
81410229820346whitefemale50-60unknownelectivedischarged_homereferral1unknownunknown0Complete30.0110.00009967532507AB+13.31unknownunknown0001False
8141149302180blackfemale40-50unknownemergencydischarged_homeemergency3unknowninternalmedicine0Complete43.0014.0000250.12198V104B+12.40unknownunknown0111False